T cell
2022-08-15
Load in libraries
library(tidyverse)
library(readxl)
library(mgcv)
library(emmeans)
library(ggeffects)
library(lme4)
library(gamlss)
library(hrbrthemes)
library(DT)
library(ggpubr)
library(cowplot)
source("scripts/utils/utils.R")Load data
# color panel
panel0 <-
c("#c85979",
"#6980ce",
"#4aac8d",
"#b460bd",
"#cd5d39",
"#b49541",
"#4ab0aa")
dosepanel <- c("#cb6939", "#8264cb")
d <- read_excel("processed_data/Tcell.xlsx")
dt <- d %>%
mutate(
animal_id = factor(animal_id),
interval = factor(interval),
day = factor(day, levels = c(64, 87, 143, 227)),
interval = factor(interval, levels = c("PBS", 0:8))
)Model fitting for CD4 data
# data analysis for CD4 cell Th1 responses
dt4 <- dt %>%
filter(cell_type == "CD4" & interval != "PBS") %>%
droplevels()
# fit zero inflated beta regression
con1 <- gamlss::gamlss.control(n.cyc = 100, trace = FALSE)
combos <- expand.grid(unique(dt4$cytokine), unique(dt4$stimulation))
# split data by cytokine and stimulation
datas <-
map2(combos$Var1, combos$Var2, ~
dt4 %>% filter(cytokine == .x & stimulation == .y)) %>%
setNames(paste(combos$Var1, combos$Var2, sep= "_" ))
# function to fit zero inflated beta regression
fit_fun <- function(dt, compare = interval) {
data = dt
if (compare == "interval") {
formula = as.formula("~ interval | day")
} else{
formula = as.formula("~ day|interval")
}
tryCatch({
fit <- gamlss::gamlss(
prop ~ day * interval,
sigma.fo = ~ day * interval,
family = BEZI,
data = data,
trace = F,
control = con1
)
em_fit <-
emmeans::emmeans(ref_grid(fit, cov.keep = c("interval", "day")),
specs = formula ,
type = "response")
},
error = function(e) {
fit <- gamlss::gamlss(
prop ~ interval + day,
sigma.fo = ~ day + interval,
family = BEZI,
data = data,
trace = F,
control = con1
)
em_fit <-
emmeans(ref_grid(fit, cov.keep = c("interval", "day")),
specs = formula ,
type = "response")
})
return(list(fit = fit, emm_fit = em_fit))
}
# obtain the model
set.seed(100)
beta_fits <-
purrr::map(datas, ~ fit_fun(dt = .x, compare = "interval"))
fits <- purrr::transpose(beta_fits)[[1]]
emmt_dat2 <-
purrr::transpose(beta_fits)[[2]] %>%
imap(~.x %>%
as_tibble() %>%
mutate(info = .y)) %>%
bind_rows() %>%
separate(info, c("cytokine", "coating"), "_")Residual diagnostics for CD4+ data
assumption_plots <-
imap(
fits ,
~ assumption_check(.x) %>%
plot_grid(
ggdraw() + draw_label(
paste0(str_replace(.y, "_", ", " ), "-specific"),
x = 0.03,
hjust = 0
),
.,
ncol = 1,
rel_heights = c(0.1, 1),
align = "v"
)
)
ggarrange(plotlist = assumption_plots)
Supplementary Figure 4AB: CD4 fold change and heatmap
#-------------------------------------------------------------------------------
# Supplementary Figure 4A: CD4 fold change
#-------------------------------------------------------------------------------
plots <-
emmt_dat2 %>%
mutate(day = as.numeric(as.character(day))) %>%
mutate(
interval = forcats::fct_recode(
interval,
"8 weeks" = "8",
"6 weeks" = "6",
"4 weeks" = "4",
"3 weeks" = "3",
"2 weeks" = "2",
"1 week" = "1",
"Prime only" = "0"
)
) %>%
split(.$cytokine) %>%
imap(
~ .x %>%
filter(day == 227) %>%
ggplot(aes(
x = interval, y = response * 100, color = interval
)) +
geom_jitter(position = position_dodge(width = 0.5), size = 2) +
geom_errorbar(
aes(ymin = asymp.LCL * 100, ymax = asymp.UCL * 100),
width = .1,
position = position_dodge(width = 0.5),
size = 1
) +
scale_color_manual(values = panel0) +
theme_bw() + theme(panel.grid.major = element_line(colour = "grey100")) +
facet_grid(day ~ coating, space = "free_x", scales = "free") +
labs(title = paste("CD4+ T cells (", .y, ")"),
subtitle = "24 weeks after the boost dose according to antigen and
mRNA-1273 dosing interval",
x = "", y = "Estimated % of CD4+ T cells") +
theme(
axis.text = element_text(size = 9),
axis.text.x = element_text(
angle = 45,
hjust = 1,
vjust = 1
),
axis.title = element_text(size = 14),
strip.text = element_text(size = 10),
legend.text = element_text(size = 14),
legend.title = element_text(size = 14),
plot.title = element_text(size = 14, face = "bold"),
panel.spacing = unit(0, "lines")
)
)
set.seed(100)
emmt_dat <-
purrr::transpose(beta_fits)[[2]] %>%
imap(
~ contrast(
regrid(regrid(.x, "response"), transform = "log10"),
"pairwise",
infer = TRUE,
adjust = "mvt",
ratio = FALSE,
level = .95
) %>% as_tibble() %>%
mutate(info = .y)
) %>%
bind_rows() %>%
separate(info, c("cytokine", "coating"), "_")
emmt_dat_reverse <-
emmt_dat %>%
mutate("significant or not" = ifelse(asymp.LCL < 0 &
asymp.UCL > 0, "no", "yes")) %>%
dplyr::select(contrast, day, estimate, cytokine, coating, `significant or not`) %>%
separate(contrast, c("g2", "g1"), " - ") %>%
mutate(estimate = -estimate)
# heat map-----------
heatmaps <-
emmt_dat %>%
mutate("significant or not" = ifelse(asymp.LCL < 0 &
asymp.UCL > 0, "no", "yes")) %>%
dplyr::select(contrast, day, estimate, cytokine, coating, `significant or not`) %>%
separate(contrast, c("g1", "g2"), " - ") %>%
bind_rows(emmt_dat_reverse) %>%
mutate(g1 = str_remove(g1, "interval"),
g2 = str_remove(g2, "interval")) %>%
split(.$cytokine) %>%
imap(
~ complete(.x, g2, g1, day, cytokine, coating) %>%
mutate("significant or not" = ifelse(
is.na(`significant or not`), "no", `significant or not`
)) %>%
dplyr::rename(`Estimated fold change` = `estimate`) %>%
filter(day == 227) %>%
ggplot(aes(
x = g2, y = g1, fill = `Estimated fold change`
)) +
scale_x_discrete(position = "top") +
scale_fill_gradient2(
na.value = "grey25",
breaks = log10(c(1 / 10, 1 / 3, 1, 3, 10)),
labels = c("1/10", "1/3", 1, 3, 10),
high = scales::muted("red"),
low = scales::muted("blue")
) +
theme_ipsum() +
geom_tile() +
geom_point(
aes(shape = `significant or not`),
color = "black",
size = 2
) +
scale_shape_manual(values = c(NA, 8)) +
labs(shape = "significantly\ndifferent?", x = "Comparison", y = "") +
facet_wrap(
~ coating,
scales = "free",
strip.position = "top",
nrow = 1
) +
ggtitle(paste("Estimated fold change in percentage of spike-specific", .y ,"-producing
CD4+ T cells")) +
theme(
strip.placement = "outside",
axis.text = element_text(size = 10),
axis.title = element_text(size = 10),
strip.text = element_text(size = 10),
legend.text = element_text(size = 12),
legend.title = element_text(size = 12)
)
)plots[[1]]
heatmaps[[1]]
Supplementary Table 6. Statistical comparisons of percentage of SARS-CoV-2 spike-specific IFNγ, IL-2, and TNFα producing CD4+ T cells through 24 weeks following dose 2.
# CD 4 table 6
set.seed(100)
emmt_table <-
purrr::transpose(beta_fits)[[2]] %>%
imap(
~ contrast(
regrid(regrid(.x, "response"), transform = "log10"),
"pairwise",
infer = TRUE,
adjust = "mvt",
level = .95
) %>% as_tibble() %>%
mutate(info = .y)
) %>%
bind_rows() %>%
separate(info, c("cytokine", "coating"), "_")
emmt_table %>%
as_tibble() %>%
dplyr::select(contrast, ratio, day, cytokine, coating, p.value) %>%
filter(p.value < 0.05) %>%
rename(
"Pairwise comparison" = contrast,
"Fold change" = "ratio",
"Cytokine" = "cytokine",
"Coating" = "coating",
"Adjusted P-value" = "p.value"
) %>%
DT::datatable(
caption = "Statistical comparisons of percentage of SARS-CoV-2
spike-specific IFNγ, IL-2, and TNFα producing CD4+ T cells through 24 weeks
following dose 2"
) %>%
formatSignif(columns = c('Fold change', 'Adjusted P-value'),
digits = 7)Supplementary Figure 12 Statistical comparisons of percentage of SARS-CoV-2 spike-specific IFNγ, IL-2, and TNFα producing CD4+ T cells through 24 weeks following dose 2 (Supplementary Table 6 visualization)
cd4_emmeans <-
emmt_table %>%
as_tibble() %>%
mutate(cytokine = case_when(
cytokine == "IFNg" ~ "IFNγ",
cytokine == "TNFa" ~ "TNFα",
TRUE ~ cytokine
)) %>%
mutate(day = case_when(
day == "64" ~ "7_1",
day == "87" ~ "30_4",
day == "143" ~ "86_12",
day == "227" ~ "169_24"
)) %>%
mutate(contrast = str_remove_all(contrast, "interval")) %>%
filter(coating == "S1" | coating == "S2") %>%
mutate(group = paste(coating, "specific", cytokine)) %>%
mutate(
"Statistically Significant" = ifelse(p.value < 0.05, "yes", "no"),
`Statistically Significant` = factor(`Statistically Significant`, levels = c("yes", "no"))
) %>%
filter(!is.na(`Statistically Significant`)) %>%
rename(
"Pairwise comparison" = contrast,
"Fold change" = "ratio",
"Adjusted P-value" = "p.value"
) %>%
split(.$day) %>%
imap(
~ .x %>%
ggplot(
aes(x = `Pairwise comparison` , y = `Fold change`, color = `Statistically Significant`)
) +
geom_point(size = 3) +
geom_errorbar(
aes(ymin = asymp.LCL, ymax = asymp.UCL),
width = .1,
size = 1
) +
theme_bw() + theme(panel.grid.major = element_line(colour = "grey100")) +
scale_y_continuous(
breaks = (c(1, 3, 10)),
labels = c(1, 3, 10),
trans = "sqrt"
) +
geom_hline(
yintercept = 1,
color = "darkcyan",
lty = "dashed"
) +
labs(
x = "",
y = "",
#title = "Estimated fold change of SARS-CoV-2 spike-specific ",
title = paste0(
str_split(.y, "_")[[1]][1],
" days post dose 2 (approximate ",
str_split(.y, "_")[[1]][2],
" week post-dose 2)"
)
) +
facet_grid(~ group,
scales = "free",
space = "free") +
coord_flip() +
theme(
strip.text = element_text(size = 14),
legend.text = element_text(size = 14),
legend.title = element_text(size = 14),
axis.text = element_text(size = 14),
legend.position = "none"
)
)
p <-
ggarrange(
cd4_emmeans$`7_1`,
cd4_emmeans$`30_4`,
cd4_emmeans$`86_12`,
cd4_emmeans$`169_24` ,
ncol = 1,
align = 'h',
labels = c('A', 'B', 'C', 'D'),
common.legend = T
)
p
Model fitting for CD8 data
# data analysis for CD8 cell Th1 responses
dt8 <- subset(dt, cell_type == "CD8" & interval !="PBS") %>%
droplevels()
# fit zero inflated beta regression
con1 <- gamlss.control(n.cyc=40, trace = FALSE)
combos <- expand.grid(unique(dt8$cytokine), unique(dt8$stimulation))
# split data by cytokine and stimulation
datas8 <-
map2(combos$Var1, combos$Var2, ~
dt8 %>% filter(cytokine == .x & stimulation == .y)) %>%
setNames(paste(combos$Var1, combos$Var2, sep= "_" ))
# obtain the model
set.seed(100)
beta_fits8 <-
purrr::map(datas8, ~ fit_fun(dt = .x, compare = "interval"))
# obtain emmeans
emmt_ind8 <-
purrr::transpose(beta_fits8)[[2]]%>%
imap(~.x %>%
as_tibble()%>%
mutate(info = .y)) %>%
bind_rows()%>%
separate(info, c("cytokine", "coating"), "_")Residual diagnostics for CD8+ data
fits <- purrr::transpose(beta_fits8)[[1]]
assumption_plots <-
imap(
fits ,
~ assumption_check(.x) %>%
plot_grid(
ggdraw() + draw_label(
paste0(str_replace(.y, "_", ", " ), "-specific"),
x = 0.03,
hjust = 0
),
.,
ncol = 1,
rel_heights = c(0.1, 1),
align = "v"
)
)
ggarrange(plotlist = assumption_plots)
Supplementary Figure 4CD: CD8 fold change
set.seed(100)
emmt_dat8 <-
purrr::transpose(beta_fits8)[[2]]%>%
imap(~contrast(regrid(regrid(.x, "response"), transform = "log10"),
"pairwise",
infer = TRUE,
adjust = "mvt",
ratio = FALSE,
level = .95)%>% as_tibble() %>%
mutate(info = .y)) %>%
bind_rows()%>%
separate(info, c("cytokine", "coating"), "_")
emmt_dat_reverse8 <-
emmt_dat8 %>%
mutate("significant or not" = ifelse(asymp.LCL<0 & asymp.UCL >0, "no", "yes"))%>%
dplyr::select(contrast, day, estimate, cytokine, coating, `significant or not`)%>%
separate(contrast, c("g2", "g1"), " - ")%>%
mutate(estimate = -estimate)
plots <-
emmt_ind8 %>%
mutate(day = as.numeric(as.character(day))) %>%
mutate(
interval = forcats::fct_recode(
interval,
"8 weeks" = "8",
"6 weeks" = "6",
"4 weeks" = "4",
"3 weeks" = "3",
"2 weeks" = "2",
"1 week" = "1",
"Prime only" = "0"
)
) %>%
split(.$cytokine) %>%
imap(
~ .x %>%
filter(day == 227) %>%
ggplot(aes(
x = interval, y = response * 100, color = interval
)) +
geom_jitter(position = position_dodge(width = 0.5), size = 2) +
geom_errorbar(
aes(ymin = asymp.LCL * 100, ymax = asymp.UCL * 100),
width = .1,
position = position_dodge(width = 0.5),
size = 1
) +
scale_color_manual(values = panel0) +
theme_bw() + theme(panel.grid.major = element_line(colour = "grey100")) +
facet_grid(day ~ coating, space = "free_x", scales = "free") +
labs(
title = paste("CD4+ T cells (", .y, ")"),
subtitle = "24 weeks after the boost dose according to antigen and
mRNA-1273 dosing interval",
x = "",
y = "Estimated % of CD8+ T cells"
) +
theme(
axis.text = element_text(size = 9),
axis.text.x = element_text(
angle = 45,
hjust = 1,
vjust = 1
),
axis.title = element_text(size = 14),
strip.text = element_text(size = 10),
legend.text = element_text(size = 14),
legend.title = element_text(size = 14),
plot.title = element_text(size = 14, face = "bold"),
panel.spacing = unit(0, "lines")
)
)
### Supplementary Figure 4D: CD8 heatmap
heatmaps <-
emmt_dat8 %>%
mutate("significant or not" = ifelse(asymp.LCL < 0 &
asymp.UCL > 0, "no", "yes")) %>%
dplyr::select(contrast, day, estimate, cytokine, coating, `significant or not`) %>%
separate(contrast, c("g1", "g2"), " - ") %>%
bind_rows(emmt_dat_reverse8) %>%
mutate(g1 = str_remove(g1, "interval"),
g2 = str_remove(g2, "interval")) %>%
split(.$cytokine) %>%
imap(
~ complete(.x, g2, g1, day, cytokine, coating) %>%
mutate("significant or not" = ifelse(
is.na(`significant or not`), "no", `significant or not`
)) %>%
dplyr::rename(`Estimated fold change` = `estimate`) %>%
filter(day == 227) %>%
ggplot(aes(
x = g2, y = g1, fill = `Estimated fold change`
)) +
scale_x_discrete(position = "top") +
scale_fill_gradient2(
na.value = "grey25",
breaks = log10(c(1 / 10, 1 / 3, 1 / 2, 1, 2, 3, 10)),
labels = c("1/10", "1/3", "1/2", 1, 2, 3, 10),
high = scales::muted("red"),
low = scales::muted("blue")
) +
theme_ipsum() +
geom_tile() +
geom_point(
aes(shape = `significant or not`),
color = "black",
size = 2
) +
scale_shape_manual(values = c(NA, 8)) +
labs(shape = "significantly\ndifferent?", x = "Comparison", y = "") +
facet_wrap(
~ coating,
scales = "free",
strip.position = "top",
nrow = 1
) +
ggtitle(
paste(
"Estimated fold change in percentage of spike-specific",
.y ,
"-producing
CD8+ T cells"
)
) +
theme(
strip.placement = "outside",
axis.text = element_text(size = 10),
axis.title = element_text(size = 10),
strip.text = element_text(size = 10),
legend.text = element_text(size = 12),
legend.title = element_text(size = 12)
)
)plots[[1]]
heatmaps[[1]]
Supplementary Table 7. Statistical comparisons of percentage of SARS-CoV-2 spike-specific IFNγ, IL-2, and TNFα producing CD8+ T cells through 24 weeks following dose 2
## CD 8 table 7
set.seed(100)
emmt_table8 <-
purrr::transpose(beta_fits8)[[2]] %>%
imap(
~ contrast(
regrid(regrid(.x, "response"), transform = "log10"),
"pairwise",
infer = TRUE,
adjust = "mvt",
level = .95
) %>% as_tibble() %>%
mutate(info = .y)
) %>%
bind_rows() %>%
separate(info, c("cytokine", "coating"), "_")
emmt_table8 %>%
as_tibble() %>%
dplyr::select(contrast, ratio, day, cytokine, coating, p.value) %>%
filter(p.value < 0.05) %>%
rename(
"Pairwise comparison" = contrast,
"Fold change" = "ratio",
"Cytokine" = "cytokine",
"Coating" = "coating",
"Adjusted P-value" = "p.value"
) %>%
DT::datatable(caption = "Statistical comparisons of percentage of SARS-CoV-2 spike-specific IFNγ, IL-2, and TNFα producing CD8+ T cells through 24 weeks following dose 2
") %>%
formatSignif(columns = c('Fold change', 'Adjusted P-value'),
digits = 7)Supplementary Figure 13 Statistical comparisons of percentage of SARS-CoV-2 spike-specific IFNγ, IL-2, and TNFα producing CD8+ T cells through 24 weeks following dose 2 (Supplementary Table 7 visualization)
cd8_emmeans <-
emmt_table8 %>%
as_tibble() %>%
mutate(cytokine = case_when(
cytokine == "IFNg" ~ "IFNγ",
cytokine == "TNFa" ~ "TNFα",
TRUE ~ cytokine
)) %>%
mutate(day = case_when(
day == "64" ~ "7_1",
day == "87" ~ "30_4",
day == "143" ~ "86_12",
day == "227" ~ "169_24"
)) %>%
mutate(contrast = str_remove_all(contrast, "interval")) %>%
filter(coating == "S1" | coating == "S2") %>%
mutate(group = paste(coating, "specific", cytokine)) %>%
mutate(
"Statistically Significant" = ifelse(p.value < 0.05, "yes", "no"),
`Statistically Significant` = factor(`Statistically Significant`, levels = c("yes", "no"))
) %>%
filter(!is.na(`Statistically Significant`)) %>%
rename(
"Pairwise comparison" = contrast,
"Fold change" = "ratio",
"Adjusted P-value" = "p.value"
) %>%
split(.$day) %>%
imap(
~ .x %>%
ggplot(
aes(x = `Pairwise comparison` , y = `Fold change`, color = `Statistically Significant`)
) +
geom_point(size = 3) +
geom_errorbar(
aes(ymin = asymp.LCL, ymax = asymp.UCL),
width = .1,
size = 1
) +
theme_bw() + theme(panel.grid.major = element_line(colour = "grey100")) +
scale_y_continuous(
breaks = (c(1, 3, 10)),
labels = c(1, 3, 10),
trans = "sqrt"
) +
geom_hline(
yintercept = 1,
color = "darkcyan",
lty = "dashed"
) +
labs(
x = "",
y = "",
#title = "Estimated fold change of SARS-CoV-2 spike-specific ",
title = paste0(
str_split(.y, "_")[[1]][1],
" days post dose 2 (approximate ",
str_split(.y, "_")[[1]][2],
" week post-dose 2)"
)
) +
facet_grid(~ group,
scales = "free",
space = "free") +
coord_flip() +
theme(
strip.text = element_text(size = 14),
legend.text = element_text(size = 14),
legend.title = element_text(size = 14),
axis.text = element_text(size = 14),
legend.position = "none"
)
)
p8 <-
ggarrange(
cd8_emmeans$`7_1`,
cd8_emmeans$`30_4`,
cd8_emmeans$`86_12`,
cd8_emmeans$`169_24` ,
ncol = 1,
align = 'h',
labels = c('A', 'B', 'C', 'D'),
common.legend = T
)
p8
Session information
sessionInfo()## R version 3.6.3 (2020-02-29)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Catalina 10.15.7
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] parallel splines stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] cowplot_1.1.1 ggpubr_0.4.0 DT_0.24 hrbrthemes_0.8.6
## [5] gamlss_5.4-3 gamlss.dist_6.0-3 MASS_7.3-51.5 gamlss.data_6.0-2
## [9] lme4_1.1-28 Matrix_1.2-18 ggeffects_1.1.3 emmeans_1.7.5
## [13] mgcv_1.8-31 nlme_3.1-144 readxl_1.4.0 forcats_0.5.1
## [17] stringr_1.4.0 dplyr_1.0.9 purrr_0.3.4 readr_2.1.2
## [21] tidyr_1.2.0 tibble_3.1.8 ggplot2_3.3.6 tidyverse_1.3.2
##
## loaded via a namespace (and not attached):
## [1] fs_1.5.2 lubridate_1.8.0 httr_1.4.3
## [4] tools_3.6.3 backports_1.4.1 bslib_0.4.0
## [7] utf8_1.2.2 R6_2.5.1 DBI_1.1.3
## [10] colorspace_2.0-3 withr_2.5.0 gridExtra_2.3
## [13] tidyselect_1.1.2 compiler_3.6.3 extrafontdb_1.0
## [16] cli_3.3.0 rvest_1.0.2 xml2_1.3.3
## [19] labeling_0.4.2 sass_0.4.2 scales_1.2.0
## [22] mvtnorm_1.1-3 systemfonts_1.0.2 digest_0.6.29
## [25] minqa_1.2.4 rmarkdown_2.14 pkgconfig_2.0.3
## [28] htmltools_0.5.3 extrafont_0.18 highr_0.9
## [31] dbplyr_2.2.1 fastmap_1.1.0 htmlwidgets_1.5.4
## [34] rlang_1.0.4 rstudioapi_0.13 farver_2.1.1
## [37] jquerylib_0.1.4 generics_0.1.3 jsonlite_1.8.0
## [40] crosstalk_1.2.0 car_3.1-0 googlesheets4_1.0.1
## [43] magrittr_2.0.3 Rcpp_1.0.9 munsell_0.5.0
## [46] fansi_1.0.3 abind_1.4-5 gdtools_0.2.3
## [49] lifecycle_1.0.1 stringi_1.7.8 yaml_2.3.5
## [52] carData_3.0-5 grid_3.6.3 crayon_1.5.1
## [55] lattice_0.20-38 haven_2.5.0 hms_1.1.1
## [58] knitr_1.39 pillar_1.8.0 boot_1.3-24
## [61] ggsignif_0.6.3 estimability_1.4.1 reprex_2.0.1
## [64] glue_1.6.2 evaluate_0.16 modelr_0.1.8
## [67] vctrs_0.4.1 nloptr_1.2.2.3 tzdb_0.3.0
## [70] Rttf2pt1_1.3.10 cellranger_1.1.0 gtable_0.3.0
## [73] assertthat_0.2.1 cachem_1.0.6 xfun_0.32
## [76] xtable_1.8-4 broom_1.0.0 rstatix_0.7.0
## [79] coda_0.19-4 survival_3.1-8 googledrive_2.0.0
## [82] gargle_1.2.0 ellipsis_0.3.2